When given a dataset, handle and prepare it for analysis or modelling:

 

1.Understanding the Problem

Define the objective: Understand the goal, such as prediction, classification, or clustering.

Familiarize with the data: Review the dataset documentation, understand the features (columns), and identify the target variable (if any).

 

2. Data Exploration (Exploratory Data Analysis - EDA)

Check data structure: Examine the dataset's shape, types of variables (categorical, numerical, etc.), and basic statistics (mean, median, standard deviation).

Visualize the data: Use plots like histograms, scatter plots, and box plots to understand data distributions, outliers, correlations, and patterns.

Identify missing values: Analyse how much data is missing, which features are affected, and decide on strategies to handle it (imputation, removal, etc.).

 

3. Data Cleaning

Handle missing values: Depending on the data, I might impute missing values using the mean/median (for numerical), the mode (for categorical), or more advanced techniques like KNN imputation.

Deal with outliers: Use statistical methods (e.g., z-score, IQR) to detect outliers and decide whether to remove or treat them (e.g., capping, transformation).

Convert categorical variables: For categorical data, I use encoding methods like one-hot encoding, label encoding, or embeddings (for large datasets).

 Ensure consistency: Standardize units, correct inconsistent data (e.g., "NA" vs. "null"), and ensure the dataset is clean and uniform.

  

4. Feature Engineering

Create new features: Derive new features based on domain knowledge or by combining existing features (e.g., creating ratios, interaction terms).

Transform features: Apply log transformation, scaling (min-max, standardization), or binning to ensure features are appropriate for the model.

Dimensionality reduction: If the dataset is high-dimensional, use techniques like Principal Component Analysis (PCA) or feature selection methods (e.g., recursive feature elimination).

 

5. Data Splitting

 Train-test split: Divide the data into training and test sets (typically 70-30 or 80-20) to ensure the model is evaluated on unseen data.

 Validation set: In more complex scenarios, I might also use a validation set or apply cross-validation to avoid overfitting and ensure model robustness.

 

6. Handling Imbalanced Data (if applicable)

 Resampling techniques: If the target variable is imbalanced, I apply techniques like SMOTE (Synthetic Minority Over-sampling Technique), under sampling, or use cost-sensitive algorithms to address this.

 

7. Model Building or Analysis

 Select the right model: Based on the problem type (regression, classification, clustering, etc.), I choose appropriate models.

 Training the model: Train the selected models and tune their hyperparameters (e.g., using grid search, random search, or Bayesian optimization).

 Evaluation: Assess model performance using appropriate metrics (accuracy, precision, recall, F1-score, ROC-AUC for classification; RMSE, MAE for regression).

  

8. Iterate and Improve

 Error analysis: Analyse where the model makes mistakes and use that to improve the model or data preparation process.

 Feature importance: Use model explainability techniques (like SHAP or LIME) to understand the most influential features and optimize further.

 

9. Deployment (if needed)

 After cleaning and building the model, I prepare the dataset for deployment by exporting it in a suitable format (e.g., CSV, database), and if necessary, set up pipelines for real-time